Abstract
License plate restriction (LPR) policy presents the most straightforward way to reduce road traffic and emissions worldwide. However, in practice, it has aroused great controversy. This policy broke the original structure of the urban transportation mode, which needed some matching strategies to adapt to this change. Investigating this travel demand change is a challenging task because it is greatly influenced by features of the local built environment. Fourteen variables from four dimensions, location, land-use diversity, distance to transit, and street design, are used to depict the built environment; moreover, the severe collinearity underlies these feature variables. To solve the multicollinearity among the variables and high-dimensional problem, this study utilizes two different penalization-based regression models, the LASSO (least absolute shrinkage and selection operator) and Elastic Net regression algorithms, to achieve the variable selection and explore the impacts of the built environment on the change of travel demand triggered by the LPR policy. Travel demand changes are assessed by the relative variation in taxi ridership in each traffic analysis zone based on the taxi GPS data. Built environment variables are measured using the transportation network data and the Baidu Map Service points of interest (POI) data. The results show that regions with a higher level of public transportation service and a higher degree of the land mix have a stronger resilience to the vehicle restriction policy. Besides, the contribution rate of public transportation is stable as a whole, while the contribution rate of richness depends on specific types of land use. The conclusions in this study can provide in-depth insights into the influence of the LPR policy and underpin traffic complementary policies to ensure the effectiveness of LPR.
Highlights
In the past several decades, the drastic growth of private car ownership and usage has caused some vexing problems, such as traffic congestion and environmental pollution [1, 2]
We further investigate the local variation of taxi ridership in a fine-grained spatial perspective. e relative variation (RV) in ridership is used to measure the change of travel demand after the vehicle restriction policy and can eliminate the magnitude effects. e relative variation in each traffic analysis zones (TAZs) is calculated as shown in equation (1)
Given the intrinsic relationship between taxis and public transport, this study stands by the position of the citywide taxi services to rethink the shortcomings of public transportation service under the vehicle restriction policy. e two types of penalization-based regression models, the LASSO and Elastic Net regression models, were employed to explore how the built environment influences the mode shift of commuting travel, using taxi GPS data covering the periods before and after the ODPW policy implementation in Xi’an
Summary
In the past several decades, the drastic growth of private car ownership and usage has caused some vexing problems, such as traffic congestion and environmental pollution [1, 2]. To improve the urban living environment, many cities have attempted to implement various traffic demand management (TDM) policies [4] to mitigate these problems. Ese strategies can be divided by purpose into two categories: (1) the control of private car ownership from the source through the license plate lottery or license plate auction policies implemented in Beijing, Shanghai, and Guangzhou [5] and (2) the reduction of private car usage through congestion pricing [6] and license plate restriction (LPR) policies. Beijing implemented a combination of the license plate lottery and LPR policies
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
